{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 什么是加性模型(additive model)?\n",
    "\n",
    "就是将几个分类函数加权相加的模型。\n",
    "$$\n",
    "H(\\boldsymbol{x})=\\sum_{t=1}^T \\alpha_t h_t(\\boldsymbol{x})\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report, f1_score\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state=42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24caa6f3220>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_circles(n_samples=300, noise=0.1, factor=0.5, random_state=random_state)\n",
    "\n",
    "plt.scatter(X[y == 0, 0], X[y == 0, 1], c='blue', s=25, label='0')\n",
    "plt.scatter(X[y == 1, 0], X[y == 1, 1], c='red', s=25, label='1')\n",
    "plt.xlim((-1.5, 1.5))\n",
    "plt.ylim((-1.5, 1.5))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((240, 2), (60, 2))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(max_iter=1000, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  系数: [[ 0.18605983 -0.03980644]]\n",
      "  截距: [0.0029855]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.45      0.43      0.44        30\n",
      "           1       0.45      0.47      0.46        30\n",
      "\n",
      "    accuracy                           0.45        60\n",
      "   macro avg       0.45      0.45      0.45        60\n",
      "weighted avg       0.45      0.45      0.45        60\n",
      "\n",
      "F1: 0.45901639344262296\n"
     ]
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(f\"  系数: {clf.coef_}\")\n",
    "print(f\"  截距: {clf.intercept_}\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('F1:', f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_results(X, y, model, title):\n",
    "    \"\"\"可视化决策边界\"\"\"\n",
    "    plt.figure(figsize=(10, 8))\n",
    "\n",
    "    # 创建网格点\n",
    "    h = 0.001\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "\n",
    "    # 预测网格点的类别\n",
    "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    # 绘制决策边界\n",
    "    plt.contourf(xx, yy, Z, alpha=0.8)\n",
    "    plt.contour(xx, yy, Z, levels=[0.5], linewidths=2, colors='black')\n",
    "\n",
    "    # 绘制数据点\n",
    "    plt.scatter(X[y == 0, 0], X[y == 0, 1], c='blue', s=25, label='0')\n",
    "    plt.scatter(X[y == 1, 0], X[y == 1, 1], c='red', s=25, label='1')\n",
    "\n",
    "    plt.title(title, fontsize=16)\n",
    "    plt.xlim((-1.5, 1.5))\n",
    "    plt.ylim((-1.5, 1.5))\n",
    "    plt.legend()\n",
    "\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\xuhao\\\\.conda\\\\envs\\\\ml\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_results(X, y, clf, 'logistic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "基础分类器系数和权重:\n",
      "分类器 1:\n",
      "  权重: 0.0250\n",
      "  系数: [[ 0.01380836 -0.00382973]]\n",
      "  截距: [4.88647857e-05]\n",
      "\n",
      "分类器 2:\n",
      "  权重: 0.0127\n",
      "  系数: [[ 0.01116663 -0.00310291]]\n",
      "  截距: [-6.41797441e-06]\n",
      "\n",
      "分类器 3:\n",
      "  权重: 0.0064\n",
      "  系数: [[ 0.00982678 -0.00272088]]\n",
      "  截距: [8.61283635e-06]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 创建基础逻辑回归分类器\n",
    "base_estimator = LogisticRegression(\n",
    "    max_iter=10000,\n",
    "    # random_state=42,\n",
    "    solver='liblinear',  # 使用liblinear求解器确保正确处理样本权重\n",
    "    C=1.0,  # 正则化强度\n",
    "    class_weight='balanced'  # 平衡类别权重\n",
    ")\n",
    "\n",
    "# 创建并训练AdaBoost模型\n",
    "ada = AdaBoostClassifier(\n",
    "    base_estimator=base_estimator,\n",
    "    n_estimators=10,  # 增加分类器数量\n",
    "    learning_rate=0.5,  # 降低学习率，使权重调整更平滑\n",
    "    algorithm='SAMME',  # 使用SAMME算法，适合二分类问题\n",
    "    random_state=42\n",
    ")\n",
    "    \n",
    "ada.fit(X_train, y_train)\n",
    "# 打印每个基础分类器的系数和权重\n",
    "print(\"基础分类器系数和权重:\")\n",
    "for i, (clf, alpha) in enumerate(zip(ada.estimators_, ada.estimator_weights_)):\n",
    "    print(f\"分类器 {i + 1}:\")\n",
    "    print(f\"  权重: {alpha:.4f}\")\n",
    "    print(f\"  系数: {clf.coef_}\")\n",
    "    print(f\"  截距: {clf.intercept_}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.40      0.42        30\n",
      "           1       0.45      0.50      0.48        30\n",
      "\n",
      "    accuracy                           0.45        60\n",
      "   macro avg       0.45      0.45      0.45        60\n",
      "weighted avg       0.45      0.45      0.45        60\n",
      "\n",
      "F1: 0.47619047619047616\n"
     ]
    }
   ],
   "source": [
    "y_pred2 = ada.predict(X_test)\n",
    "print(classification_report(y_test, y_pred2))\n",
    "print('F1:', f1_score(y_test, y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "False\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "False\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "for x, y in zip(y_pred, y_pred2):\n",
    "    print(x == y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\xuhao\\\\.conda\\\\envs\\\\ml\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_results(X, y, ada, 'adaboost')"
   ]
  }
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